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All Journal JIKO (Jurnal Informatika dan Komputer) International Journal of Healthcare and Information Technology
Raditya Arief Pratama
Politeknik Negeri Jember
Computer Science & IT Public Health

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Rancang Bangun Aplikasi Pelaporan Pemadam Kebakaran Berbasis Android Raditya Arief Pratama; Qonitatul Hasanah; Puji Hastuti
JURNAL INFORMATIKA DAN KOMPUTER Vol 7, No 1 (2023): Februari 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (680.64 KB) | DOI: 10.26798/jiko.v7i1.626

Abstract

Pemadam kebakaran (Damkar), Branwir, atau PMK merupakan petugas atau dinas yang dilatih dan bertugas untuk menanggulangi kebakaran. Selain menanggulangi kebakaran, petugas pemadam kebakaran (damkar) juga dilatih untuk menyelamatkan korban dari bencana alam, gedung runtuh, kecelakaan lalu lintas, dan lain-lain [1]. Sayangnya, masyarakat masih terkendala dalam melaporkan kebakaran kepada petugas karena hanya tersedia melalui telepon. Sering kali satuan pemadam kebakaran terlambat menuju lokasi penyelamatan. Hal ini dapat disebabkan karena empat hal. Pertama, petugas kurang siap siaga. Kedua, lalu lintas yang padat saat perjalanan menuju lokasi kejadian. Ketiga, warga yang mengalami bencana terlambat memberikan informasi kepada petugas. Keempat, informasi lokasi yang didapat dari masyarakat kurang jelas. Diharapkan aplikasi ini mampu menjembatani pihak masyarakat dan petugas damkar dalam melaporkan dan menerima kejadian kebakaran, bencana alam. Selama terkoneksi dengan internet, maka aplikasi ini dapat melaporkan kejadian di lingkungan sekitarnya kepada pihak dinas Pemadam Kebakaran. Terdapat beberapa fitur utama yang dapat memudahkan masyarakat untuk melapor diantaranya GPS Foto Tagging, dan Google Maps. Fitur tersebut nantinya akan memperoleh informasi yang dilaporkan dari masyarakat berbentuk foto beserta lokasi diambilnya foto, dan masyarakat dapat melihat posisi terkini petugas damkar yang menuju lokasi kejadian. 
Intelligent System for Early Detection of Heart Disease Using XGBoost Machine Learning Algorithm on Web Application Intan Sulistyaningrum Sakkinah; Puji Hastuti; Muhammad Ainul Fikri; Ulfa Emi Rahmawati; Raditya Arief Pratama; Maulana Akbar Firdausya; Ratna Indah Anggraini
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6685

Abstract

Heart disease remains a major contributor to global mortality, highlighting the importance of effective early detection systems that can assist both clinicians and general users. This study develops a heart disease prediction model based on the XGBoost algorithm and deploys it within a web-based application to enhance accessibility and practical usability. The research uses a dataset of 918 instances containing 12 demographic and clinical features commonly associated with cardiovascular risk. Pearson correlation analysis was performed to assess feature relevance, revealing that ExerciseAngina, Oldpeak, ST_Slope, Age, and MaxHR exhibit the strongest correlations with the HeartDisease outcome. These findings align with established clinical evidence on exercise-induced angina, ST-segment depression, and cardiac functional capacity. Following preprocessing and feature encoding, the XGBoost model was trained and evaluated. The model achieved strong predictive performance, with 88.26% accuracy, 88.32% precision, 91.66% recall, an F1-score of 89.96%, and an ROC-AUC of 0.93. The results demonstrate that XGBoost effectively discriminates between positive and negative cases and provides a good balance between sensitivity and precision. To enable real-world applicability, the final model was deployed on a Flask backend and integrated into a web application that allows users to input clinical parameters and receive real-time predictions. System testing confirmed that the application accurately delivers outputs and functions reliably across different input conditions. Overall, this study shows the feasibility of combining machine learning with web technologies to support early, accessible heart disease screening. Future work will involve usability testing and validation using real patient data to further strengthen the system’s clinical relevance.
Co-Authors Intan Sulistyaningrum Sakkinah Maulana Akbar Firdausya Muhammad Ainul Fikri Puji Hastuti Qonitatul Hasanah Ratna Indah Anggraini Ulfa Emi Rahmawati